Indiana University-Purdue University Indianapolis (IUPUI) / Multi-agent systems consist of multiple agents that interact and coordinate with each other to work towards to certain goal. Multi-agent systems naturally arise in a variety of domains such as robotics, telecommunications, and economics. The dynamic and complex nature of these systems entails the agents to learn the optimal solutions on their own instead of following a pre-programmed strategy. Reinforcement learning provides a framework in which agents learn optimal behavior based on the response obtained from the environment. In this thesis, we propose various novel de- centralized, learning automaton based algorithms which can be employed by a group of interacting learning automata. We propose a completely decentralized version of the estimator algorithm. As compared to the completely centralized versions proposed before, this completely decentralized version proves to be a great improvement in terms of space complexity and convergence speed. The decentralized learning algorithm was applied; for the first time; to the domains of distributed object tracking and distributed watershed management. The results obtained by these experiments show the usefulness of the decentralized estimator algorithms to solve complex optimization problems. Taking inspiration from the completely decentralized learning algorithm, we propose the novel concept of partial decentralization. The partial decentralization bridges the gap between the completely decentralized and completely centralized algorithms and thus forms a comprehensive and continuous spectrum of multi-agent algorithms for the learning automata. To demonstrate the applicability of the partial decentralization, we employ a partially decentralized team of learning automata to control multi-agent Markov chains. More flexibility, expressiveness and flavor can be added to the partially decentralized framework by allowing different decentralized modules to engage in different types of games. We propose the novel framework of heterogeneous games of learning automata which allows the learning automata to engage in disparate games under the same formalism. We propose an algorithm to control the dynamic zero-sum games using heterogeneous games of learning automata.

Indiana University-Purdue University Indianapolis (IUPUI) / In this thesis, Artificial Neural Network (ANN) technique is used to model and simulate the Turning Process. Significant machining parameters (i.e. spindle speed, feed rate, and, depths of cut) and process parameters (surface roughness and cutting forces) are considered. It is shown that Multi-Layer Back Propagation Neural Network is capable to perform this particular task. Design of Experiments approach is used for efficient selection of values of parameters used during experiments to reduce cost and time for experiments. The Particle Swarm Optimization methodology is used for constrained optimization of machining parameters to minimize surface roughness as well as cutting forces. ANN and Particle Swarm Optimization, two computational intelligence techniques when combined together, provide efficient computational strategy for finding optimum solutions. The proposed method is capable of handling multiple parameter optimization problems for processes that have non-linear relationship between input and output parameters e.g. milling, drilling etc. In addition, this methodology provides reliable, fast and efficient tool that can provide suitable solution to many problems faced by manufacturing industry today.

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We look at robotic systems made of separate discrete components that, by self-assembling, can organize into physical structures of growing size. We review 22 such systems, exhibiting components ranging from passive mechanical parts to mobile<p>robots. We present a taxonomy of the systems, and discuss their design and function. We then focus on a particular system, the swarm-bot. In swarm-bot, the components that assemble are self-propelled modules that are fully autonomous in power, perception, computation, and action. We examine the additional capabilities and functions self-assembly can offer an autonomous group of modules for the accomplishment of a concrete task: the transport of an object. The design of controllers is accomplished in simulation using<p>techniques from biologically-inspired computing. We show that self-assembly can offer adaptive value to groups that compete in an artificial evolution based on their fitness in task performance. Moreover, we investigate mechanisms that facilitate the design of self-assembling systems. The controllers are transferred to the physical swarm-bot system, and the capabilities of self-assembly and object transport are extensively evaluated in a range of different environments. Additionally, the controller for self-assembly is transferred and evaluated on a different robotic system, a super-mechano colony. Given the breadth and quality of the results obtained, we can say that the swarm-bot qualifies as the current state of the art in self-assembling robots. Our work supplies some initial evidence (in form of simulations and experiments with the swarm-bot) that self-assembly can offer robotic systems additional capabilities and functions useful for the accomplishment of concrete tasks.<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

L'intelligence en essaim constitue désormais un domaine à part entière de l'intelligence artificielle distribuée. Les problématiques qu'elle soulève touchent cependant à de nombreux autres domaines ou questions scientifiques. En particulier le concept d'essaim trouve pleinement sa place au sein de la science dites des ``systèmes complexes''. Cette thèse présente ainsi la conception, les caractéristiques et les applications d'un modèle original, le système multi-agent logistique (SMAL), pour le domaine de l'intelligence en essaim. Le SMAL trouve son origine en modélisation des systèmes complexes : il est en effet issu des réseaux d'itérations logistiques couplées dont nous avons adapté le modèle de calcul au schéma ``influence-réaction'' des systèmes multi-agents. Ce modèle est fondé sur des principes communs à d'autres disciplines, comme la synchronisation et le contrôle paramétrique, que nous plaçons au coeur des mécanismes d'auto-organisation et d'adaptation du système. L'environnement à base de champs est l'autre aspect fondamental du SMAL, en permettant la réalisation des interactions indirectes des agents et en jouant le rôle d'une structure de données pour le système. Les travaux décrits dans cette thèse donnent lieu à des applications principalement en simulation et en optimisation combinatoire.L'intérêt et l'originalité du SMAL pour l'intelligence en essaim résident dans l'aspect générique de son schéma théorique qui permet de traiter avec un même modèle des phénomènes considérés a priori comme distincts dans la littérature : phénomènes de ``flocking'' et phénomènes stigmergiques ``fourmis'' à base de phéromones. Ce modèle répond ainsi à un besoin d'explication des mécanismes mis en jeu autant qu'au besoin d'en synthétiser les algorithmes générateurs. / Swarm Intelligence is from now on a full part of Distributed Artificial Intelligence. Its associated problematics meet many other fields and scientific questions. The concept of swarm in particular belongs to the science called the science of complex systems. This phd thesis shows the design and the characteristics and the applications of a novel type of model called the logistic multi-agent system (LMAS) dedicated to the Swarm Intelligence field. The LMAS has its foundations in complex system modeling: it is inspired from the coupled logistic map lattice model which has been adapted to the ``Influence-Reaction'' modeling of multi-agent systems. This model is based on universal principles such as synchronization and parametric control which are considered as the main mechanisms of self-organization and adaptation in the heart of the system. The field-layered based environment is the other important feature of the LMAS, since it enables indirect interactions and plays the part of a data structure for the whole system. The work of this thesis is put into practice for simulation and optimization.The novelty of the LMAS lies in its generic theoretical framework, which enables to tackle problems considered as distinct in the literature, in particular flocking and ant-like stigmergic behavior. This model meets the need of explaining basic mechanisms and the need of synthesizing generative algorithms for the Swarm Intelligence.

Swarm robotics is the study of developing and controlling large groups of robots. Collectives of robots possess advantages over single robots such as being robust to mission failures due to single-robot errors. Experimental research in swarm robotics is currently limited by swarm robotic technology. Current swarm robotic systems are either small groups of sophisticated robots or large groups of simple robots due to manufacturing overhead, functionality-cost dependencies, and their need to avoid collisions, amongst others. It is therefore useful to develop a swarm robotic system that is easy to manufacture, that utilises its sensors beyond standard usage, and that allows for physical interactions. In this work, I introduce a new type of low-friction locomotion and show its first implementation in the HoverBot system. The HoverBot system consists of an air-levitation and magnet table, and a HoverBot agent. HoverBots are levitating circuit boards which are equipped with an array of planar coils and a Hall-effect sensor. HoverBot uses its coils to pull itself towards magnetic anchors that are embedded into a levitation table. These robots consist of a Printed Circuit Board (PCB), surface mount components, and a battery. HoverBots are easily manufacturable, robots can be ordered populated; the assembly consists of plugging in a battery to a robot. I demonstrate how HoverBot's low-cost hardware can be used beyond its standard functionality. HoverBot's magnetic field readouts from its Hall-effect sensor can be associated with successful movement, robot rotation and collision measurands. I build a time series classifier based on these magnetic field readouts, I modify and apply signal processing techniques to enable the online classification of the time-variant magnetic field measurements on HoverBot's low-cost microcontroller. This method allows HoverBot to detect rotations, successful movements, and collisions by utilising readouts from its single Hall-effect sensor. I discuss how this classification method could be applied to other sensors and demonstrate how HoverBots can utilise their classifier to create an occupancy grid map. HoverBots use their multi-functional sensing capabilities to determine whether they moved successfully or collided with a static object to map their environment. HoverBots execute an "explore-and-return-to-nest" strategy to deal with their sensor and locomotion noise. Each robot is assigned to a nest (landmark); robots leave their nests, move n steps, return and share their observations. Over time, a group of four HoverBots collectively builds a probabilistic belief over its environment. In summary, I build manufacturable swarm robots that detect collisions through a time series classifier and map their environment by colliding with their surroundings. My work on swarm robotic technology pushes swarm robotics research towards studies on collision-dependent behaviours, a research niche that has been barely studied. Collision events occur more often in dense areas and/or large groups, circumstances that swarm robots experience. Large groups of robots with collision-dependent behaviours could become a research tool to help invent and test novel distributed algorithms, to understand the dependencies between local to global (emergent) behaviours and more generally the science of complex systems. Such studies could become tremendously useful for the execution of large-scale swarm applications such as the search and rescue of survivors after a natural disaster.

<p>The Ph.D. thesis addresses a comprehensive study of the bee colony<br />optimization meta-heuristic method (BCO). Theoretical analysis of the<br />method is conducted with the tools of probability theory. Necessary and<br />sufficient conditions are presented that establish convergence of the BCO<br />method towards an optimal solution. Three parallelization strategies and five<br />corresponding implementations are proposed for BCO for distributed-memory<br />systems. The influence of method&rsquo;s parameters on the performance of the<br />BCO algorithm for two combinatorial optimization problems is analyzed<br />through the experimental study.</p> / <p>Докторска дисертације се бави испитивањем метахеуристичке методе<br />оптимизације колонијом пчела. Извршена је теоријска анализа<br />асимптотске конвергенције методе посматрањем конвергенције низа<br />случајних променљивих. Установљени су довољни и потребни услови<br />за које метода конвергира ка оптималном решењу. Предложене су три<br />стратегије паралелизације и пет одговарајућих имплементација конст-<br />руктивне варијанте методе за рачунаре са дистрибуираном меморијом.<br />Извршено је експериментално испитивање утицаја параметара методе<br />на њене перформансе за два различита комбинаторна проблема:<br />проблем распоређивања и проблем задовољивости.</p> / <p>Doktorska disertacije se bavi ispitivanjem metaheurističke metode<br />optimizacije kolonijom pčela. Izvršena je teorijska analiza<br />asimptotske konvergencije metode posmatranjem konvergencije niza<br />slučajnih promenljivih. Ustanovljeni su dovoljni i potrebni uslovi<br />za koje metoda konvergira ka optimalnom rešenju. Predložene su tri<br />strategije paralelizacije i pet odgovarajućih implementacija konst-<br />ruktivne varijante metode za računare sa distribuiranom memorijom.<br />Izvršeno je eksperimentalno ispitivanje uticaja parametara metode<br />na njene performanse za dva različita kombinatorna problema:<br />problem raspoređivanja i problem zadovoljivosti.</p>

Worldwide concern about the environmental problems and a possible energy crisis has led to increasing interest in clean and renewable energy generation. Among various renewable energy sources, wind power is the most rapidly growing one. Therefore, how to provide efficient, reliable, and high-performance wind power generation and distribution has become an important and practical issue in the power industry. In addition, because of the new constraints placed by the environmental and economical factors, the trend of power system planning and operation is toward maximum utilization of the existing infrastructure with tight system operating and stability margins. This trend, together with the increased penetration of renewable energy sources, will bring new challenges to power system operation, control, stability and reliability which require innovative solutions. Flexible ac transmission system (FACTS) devices, through their fast, flexible, and effective control capability, provide one possible solution to these challenges. To fully utilize the capability of individual power system components, e.g., wind turbine generators (WTGs) and FACTS devices, their control systems must be suitably designed with high reliability. Moreover, in order to optimize local as well as system-wide performance and stability of the power system, real-time local and wide-area coordinated control is becoming an important issue. Power systems containing conventional synchronous generators, WTGs, and FACTS devices are large-scale, nonlinear, nonstationary, stochastic and complex systems distributed over large geographic areas. Traditional mathematical tools and system control techniques have limitations to control such complex systems to achieve an optimal performance. Intelligent and bio-inspired techniques, such as swarm intelligence, neural networks, and adaptive critic designs, are emerging as promising alternative technologies for power system control and performance optimization. This work focuses on the development of advanced optimization and intelligent control algorithms to improve the stability, reliability and dynamic performance of WTGs, FACTS devices, and the associated power networks. The proposed optimization and control algorithms are validated by simulation studies in PSCAD/EMTDC, experimental studies, or real-time implementations using Real Time Digital Simulation (RTDS) and TMS320C6701 Digital Signal Processor (DSP) Platform. Results show that they significantly improve electrical energy security, reliability and sustainability.

Le concept du « smart » envahit de plus en plus notre vie quotidienne. L’exemple type est sans doute le smartphone. Celui-ci est devenu au fil des ans un appareil incontournable. Bientôt, c’est la ville, la voiture, la maison qui seront « intelligentes ». L’intelligence se manifeste par une capacité d’interaction et de prise de décision entre l’environnement et l’utilisateur. Ceci nécessite des informations sur les changements d’états survenus des deux côtés. Les réseaux de capteurs permettent de collecter ces données, de leur appliquer des pré-traitements et de les transmettre aux applications. Ces réseaux de par certaines de leurs caractéristiques se rapprochent de l’intelligence collective, dans le sens, où des entités de faibles capacités se coordonnent automatiquement, sans intervention humaine, de façon décentralisée et distribuée pour accomplir des tâches complexes. Ces méthodes bio-inspirées ont servi à la résolution de plusieurs problèmes, surtout l’optimisation, ce qui nous a encouragé à étudier la possibilité de les utiliser pour les problèmes liés à l’Ambient Assisted Living ou AAL et à la classification automatique de données. L’AAL est un sous-domaine des services dits basés sur le contexte, et a pour objectifs de faciliter la vie des personnes âgées et handicapées dans leurs défis quotidiens. Pour ce faire, il détermine le contexte et, sur cette base, propose divers services. Deux éléments du contexte nous ont intéressé : le handicap et la position. Bien que la détermination de la position (localisation, positionnement) se fasse à l’extérieur des bâtiments avec des précisions très satisfaisantes, elle rencontre plusieurs difficultés à l’intérieur des bâtiments, liées à la propagation des ondes électromagnétiques dans les milieux difficiles, aux coûts des systèmes, à l’interopérabilité, etc. Nos travaux se sont intéressés au positionnement des personnes handicapées à l’intérieur de bâtiments en utilisant un réseau de capteurs afin de déterminer les caractéristiques de l’onde électromagnétique (puissance, temps, angle) pour estimer la position par méthodes géométriques (triangulation, latération), méthodes de fingerprinting (k plus proches voisins), par des filtres baysiens (filtre de Kalman). L’application est d’offrir des services types AAL tel que la navigation. Nous avons élargi la notion de réseau de capteurs pour prendre en compte tout appareil capable d’émettre et de recevoir une onde électromagnétique et se trouvant dans l’environnement. Nous avons aussi appliqué l’algorithme API sur la classification automatique de données. Enfin, nous avons proposé une architecture à middleware pour la localisation indoor. / The concept of « smart » invades more and more our daily life. A typical example is the smartphone, which becames by years an essential device. Soon, it’s the city, the car and the home which will become « smart ». The intelligence is manifested by the ability for the environment to interact and to take decisons in its relationships with users and other environments. This needs information on state changes occurred on both sides. Sensor networks allow to collect these data, to apply on them some pre-processings and to transmit them. Sensor network, towards some of their caracteristics are closed to Swarm Intelligence in the sense that small entities with reduced capababilities can cooperate automatically, in unattended, decentralised and distributed manner in order to accomplish complex tasks. These bio-inspired methods have served as basis for the resolution of many problems, mostly optimization and this insipired us to apply them on problems met in Ambient Assisted Living and on the data clustering problem. AAL is a sub-field of context-aware services, and its goals are to facilitate the everyday life of elderly and disable people. These systems determine the context and then propose different kind of services. We have used two important elements of the context : the position and the disabilty. Although positioning has very good precision outdoor, it faces many challenges in indoor environments due to the electromagnetic wave propagation in harsh conditions, the cost of systems, interoperabilty, etc. Our works have been involved in positioning disabled people in indoor environment by using wireless sensor network for determining the caracteristics of the electromagnetic wave (signal strenght, time, angle) for estimating the position by geometric methods (triangulation, lateration), fingerprinting methods (k-nearest neighbours), baysiens filters (Kalman filter). The application is to offer AAL services like navigation. Therefore we extend the definition of sensor node to take into account any device, in the environment, capable of emiting and receiving a signal. Also, we have studied the possibility of using Pachycondylla Apicalis for data clustering and for indoor localization by casting this last problem as data clustering problem. Finally we have proposed a system based on a middleware architecture.